Analysis of the Dimensionality Issues in House Price Forecasting Modeling

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

“…Considering economic-and management criteria, the use of AI in general allows to improve the value proposition of the firm, through customization, value co-creation, search optimization or optimization of processes (Moreno-Izquierdo et al, 2018), which allows to enhance the customer value, understood from the customer perspective, as well as from the firm perspective on customer value. Furthermore, the accuracy of forecasts is a critical element for decision making of investment and using AI can significantly improve the valuation accuracy, but often needs to be traded off with interpretability and does not always return profitable covering the realized investment (Sakri and Ali, 2022;Liu et al, 2018;Moreno-Izquierdo et al, 2018). The cost saving of predictions using AI algorithms comes especially in terms of time savings in the price prediction, which happens fast or even in real-time (Agrawal et al, 2022).…”

“…changes the approach to price functions, suggesting the pattern instead of imposing a particular (linear) algorithm, but is based on the same input elements as the hedonic pricing models. Several studies have compared the performance of hedonic pricing and AI based methods and find improved accuracy of the value forecast and a reduced price prediction error (Sakri and Ali, 2022;Moreno-Izquierdo et al, 2018;Liu et al, 2018). The trade-off comes often in terms of interpretability, which remains a black-box in many AI algorithms, while being easy for a hedonic approach.…”

“…Personalized pricing, as detailed by Gerlick and Liozu (2020), highlights the pivotal role of AI and algorithmic decision-making in creating price settings that adjust specifically to individual consumer behaviours and preferences. AI based personalized pricing allows to analyse vast consumer data sets, enabling dynamic, real-time price adjustments for (Sakri and Ali, 2022;Liu et al, 2018;Moreno-Izquierdo et al, 2018) 7. Cost savings and efficiency (Agrawal et al, 2022) 8.…”

A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament

“…The model will not target irrelevant features when building the model. Furthermore, feature selection may enhance model accuracy by removing irrelevant features [6]. Since only the relevant features are selected in the model, the runtime can be reduced by decreasing the dimension of the data and running the models more quickly [7].…”

“…Most of the prior research on different areas of interest has proven that feature selection before model construction can aid in facilitating the selection of more relevant and correlated features to minimize the overfitting of the model with excessively irrelevant features [5], [6], [34], [35], [36]. Hence, we are applying feature selection techniques on this tennis match status to determine which factors are most significant in winning the tennis match, and we are using only the essential variables for our model formulation rather than incorporating all variables.…”